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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS)
Vu, Tuan Anh
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Wireless power transfer through metal using inductive link Vu, Tuan Anh; Pham, Chi Van; Pham, Anh-Vu; Gardner, Christopher S.
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 10, No 4: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1727.982 KB) | DOI: 10.11591/ijpeds.v10.i4.pp1906-1913

Abstract

This paper presents a highly efficient power transfer system based on a co-design of a class-E power amplifier (PA) and a pair of inductively coupled Helical coils for through-metal-wall power transfer. Power is transferred wirelessly through a 3.1-mm thick aluminum barrier without any physical penetration and contact. Measurement results show that the class-E PA achieves a peak power gain of 25.2 dB and a maximum collector efficiency of 57.3%, all at 200 Hz. The proposed system obtains a maximum power transfer efficiency of 9% and it can deliver 5 W power to the receiver side through the aluminum barrier.
Sepsis detection using biomarkers and machine learning Vu, Tuan Anh; Bac, Dang Hoai; Nguyen, Minh Tuan
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1286-1297

Abstract

Life-threatening dysfunction of organs, known as sepsis, is caused by an imbalanced response of host to infection. In this work, an efficient algorithm is proposed to address vital biomarkers for identification of sepsis using immune-related differential expression genes. A total of 16 gene datasets are processed for the extraction of a gene intersection between different gene datasets and the immune-related gene group, which improve the generalization of the final detection algorithm due to diversity of the input data. A novel gene selection method using sequential forward gene selection, machine learning, and ranked genes based on their importance calculated by a random forest model. A subset of 36 potential immune-related genes, which are identified as the biomarkers from 560 input genes, show an efficiency of the proposed gene selection algorithm. The biomarkers are validated the performance using various machine learning and deep learning related to sepsis diagnosis. The highest statistical performance is shown for the random forest model using the biomarkers as the input with an accuracy of 96.83%, sensitivity of 98.86%, specificity of 86.70%, and AUC of 98.67%. The proposed detection algorithm includes a random forest model and 36 biomarkers, which is simple, effective, and reliable for the applications in clinic environments.
Co-Authors Bac, Dang Hoai Gardner, Christopher S. Nguyen, Minh Tuan Pham, Anh-Vu Pham, Chi Van